Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules.

@article{Bereau2015TransferableAM,
  title={Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules.},
  author={T. Bereau and Denis Andrienko and O. Anatole von Lilienfeld},
  journal={Journal of chemical theory and computation},
  year={2015},
  volume={11 7},
  pages={
          3225-33
        }
}
Accurate representation of the molecular electrostatic potential, which is often expanded in distributed multipole moments, is crucial for an efficient evaluation of intermolecular interactions. Here we introduce a machine learning model for multipole coefficients of atom types H, C, O, N, S, F, and Cl in any molecular conformation. The model is trained on quantum-chemical results for atoms in varying chemical environments drawn from thousands of organic molecules. Multipoles in systems with… 

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